scholarly journals Effects of control on the dynamics of an adjacent protected wolf population in interior Alaska

2017 ◽  
Vol 198 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Joshua H. Schmidt ◽  
John W. Burch ◽  
Margaret C. MacCluskie
Keyword(s):  
Wolf Population ◽  
Interior Alaska

Geologic Map of Central (Interior) Alaska

1998 ◽  
Author(s):  
Frederic H. Wilson ◽  
James H. Dover ◽  
Dwight C. Bradley ◽  
Florence R. Weber ◽  
Thomas K. Bundtzen ◽  
...  
Keyword(s):  
Interior Alaska ◽  
Geologic Map

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

Comparative drought sensitivity of co‐occurring white spruce and paper birch in interior Alaska

2021 ◽  
Author(s):  
Patrick F. Sullivan ◽  
Annalis H. Brownlee ◽  
Sarah B.Z. Ellison ◽  
Sean M.P. Cahoon
Keyword(s):  
White Spruce ◽  
Drought Sensitivity ◽  
Interior Alaska ◽  
Paper Birch

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

scholarly journals Basement Structure and Styles of Active Tectonic Deformation in Central Interior Alaska

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 127
Author(s):  
Nilesh C. Dixit ◽  
Catherine Hanks
Keyword(s):  
Crustal Structure ◽  
Foreland Basin ◽  
Active Regions ◽  
Structural Features ◽  
Tectonic Deformation ◽  
Block Rotation ◽  
High Concentration ◽  
Intraplate Earthquakes ◽  
Interior Alaska ◽  
Nenana Basin

Central Interior Alaska is one of the most seismically active regions in North America, exhibiting a high concentration of intraplate earthquakes approximately 700 km away from the southern Alaska subduction zone. Seismological evidence suggests that intraplate seismicity in the region is not uniformly distributed, but concentrated in several discrete seismic zones, including the Nenana basin and the adjacent Tanana basin. Although the location and magnitude of the seismic activity in both basins are well defined by a network of seismic stations in the region, the tectonic controls on these intraplate earthquakes and the heterogeneous nature of Alaska’s continental interior remain poorly understood. We investigated the crustal structure of the Nenana and Tanana basins using available seismic reflection, aeromagnetic and gravity anomaly data, supplemented by geophysical well logs and outcrop data. We developed nine new two-dimensional forward models to delineate internal geometries and the crustal structure of Alaska’s interior. The results of our study demonstrates a strong crustal heterogeneity beneath both basins. The Tanana basin is a relatively shallow (up to 2 km) asymmetrical foreland basin with its southern, deeper side controlled by the northern foothills of the Central Alaska Range. Northeast-trending left lateral strike-slip faults within the Tanana basin are interpreted as a zone of clockwise crustal block rotation. The Nenana basin has a fundamentally different geometry. It is a deep (up to 8 km), narrow transtensional pull-apart basin that is deforming along the left-lateral Minto Fault. This study identifies two distinct modes of current tectonic deformation in Central Interior Alaska and provides a basis for modeling the interplay between intraplate stress fields and major structural features that potentially influence the generation of intraplate earthquakes in the region.

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